
# # ACCESS GOOGLE DRIVE
# from google.colab import drive
# drive.mount('/content/drive')


# # Change Directory
# import os
# dir = '/content/drive/MyDrive/Data Analyst Projects/Outlet/Datasets'

# if os.getcwd() != dir:
#   os.chdir(dir)
#   print(f'changed directory to {dir}')
# else:
#   print(f'already in {dir}')


# import libraries
import numpy as np
import pandas as pd
from sklearn import cluster
from sklearn.cluster import KMeans
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px


# Take a look at the files
# os.chdir('Datasets')
# ! ls


membership = pd.read_csv('Membership.csv')
cards = pd.read_csv('Cards.csv')
rewards = pd.read_csv('Rewards.csv')
transactionMeta = pd.read_csv('TransactionMeta.csv')
businessCategory = pd.read_csv('BusinessCategory.csv', dtype={'Business_ID': str})
zipcodes = pd.read_csv('Zipcodes.csv')


display('membership', membership.shape)
display('cards', cards.shape)
display('rewards', rewards.shape)
display('transactionMeta', transactionMeta.shape)
display('businessCategory', businessCategory.shape)
display('Distinct businessCategory', businessCategory.Category.unique().size)
display('Annual sales', transactionMeta.Amount.sum())

'membership'

(6466, 17)

'cards'

(6467, 2)

'rewards'

(407285, 12)

'transactionMeta'

(95023, 4)

'businessCategory'

(620, 5)


# Take a look at the datasets
display('membership', membership.head(2))
display('cards', cards.head(2))
display('rewards', rewards.head(2))
display('transactionMeta', transactionMeta.head(2))
display('businessCategory', businessCategory.head(2))

'membership'

'cards'

'rewards'

'transactionMeta'

'businessCategory'


# Merge membership data with address
zipcodes['縣市'] = zipcodes['欄位2'].str[:3]
zipcodes['鄉鎮市區'] = zipcodes['欄位2'].str[3:]
zipcodes['City'] = zipcodes['欄位3'].str.split(',').str[1]
zipcodes['Town'] = zipcodes['欄位3'].str.split(',').str[0]
zipcodes.rename(columns={'欄位1': 'Zipcode'}, inplace=True)
zipcodes['Zipcode'] = zipcodes['Zipcode'].astype(str)
zipcodes

membership_loc = membership.merge(zipcodes[['Zipcode', 'City', 'Town']], 
                 how='left',
                 left_on='Zipcode', 
                 right_on='Zipcode')

# Merge membership with card ID
membership_full = membership_loc.merge(cards,
                                       how = 'inner', 
                                       left_on = 'Member_ID',
                                       right_on = 'Member_ID')
# Convert data type & handle missing values
membership_full['Age'] = [None if age == '-' else int(age) for age in membership_full['Age']]
membership_full['Gender'] = [None if sex not in ['F', 'M'] else sex for sex in membership_full['Gender']]
membership_full['Regeistered_date'] = [None if date == '-' else pd.to_datetime(date) for date in membership_full['Regeistered_date']]
membership_full['Regeistered_month'] = membership_full['Regeistered_date'].dt.month


# Clean up the Business_ID
transactionMeta['Business_ID'] = transactionMeta['Business_ID_Date'].str.split('_').str[0]

# Parse datetime
transactionMeta['Date'] = pd.to_datetime(transactionMeta['Date'])

# Join Transactions & Business Categories
transactionWithCat = transactionMeta.merge(
    businessCategory[['Business_ID', 'Category']], 
    how='left', 
    left_on='Business_ID', 
    right_on='Business_ID'
)

# This table has no primary key!!! => Group by Date & Card_ID (See the same person on the same day as 1 visit)
transactionByVisit = (transactionMeta
                         .groupby(['Card_ID','Date'], as_index=False)
                         .agg({'Amount': 'sum'})
                         )


# Encoding Campaign Type
def campaignCode(c):
  if ('三倍' in c) or ('3倍' in c):
    return 'Triple'
  elif ('兩倍' in c) or ('2倍' in c):
    return 'Double'
  elif '會員紅利積點' in c:
    return 'Normal'
  else:
    return c
rewards['Campaign_code'] = rewards['Campaign'].map(lambda c: campaignCode(c))

# Get rid of odd values & translate into English
rewards_clean = rewards[rewards['Campaign_code'].isin(['Normal', 'Double', 'Triple'])]
rewards_clean = rewards_clean.rename(columns={'集點':'points'})
rewards_clean['points'] = rewards_clean['points'].astype(int)


# Calculating Recency, Frequency & Monetary
RFM = (transactionByVisit
           .groupby('Card_ID')
           .agg(Most_recent=('Date','max'),
                Frequency=('Date','size'), 
                Monetary=('Amount','mean'))
           )
# Feature Engineering
RFM['Recency'] = (pd.Timestamp(2020, 1, 1, 0) - RFM['Most_recent']).dt.days
RFM['log(Monetary)'] = np.log(RFM['Monetary'])
RFM['Monetary_Annual'] = RFM['Frequency'] * RFM['Monetary']

# Retrieve The design matrix for model building
RFM_design = RFM[['Recency', 'Frequency', 'log(Monetary)']]

# Standardize RFM values
means = np.mean(RFM_design, axis = 0)
std = np.std(RFM_design, axis = 0)
RFM_standardized = (RFM_design - means) / std
RFM_standardized

# Check the distribution of RFM
def plot_rfm(df, name=''):
  fig, ax = plt.subplots(1, 3, figsize=(12,5))
  fig.suptitle(f'{name} RFM distribution', fontsize=20)

  for i, value in enumerate(['Recency', 'Frequency', 'log(Monetary)']):
    ax[i].hist(df[value])
    ax[i].set_title(value)
    
# Plot original RFM
plot_rfm(RFM_design, 'Original')
# Plot standardized RFM
plot_rfm(RFM_standardized, 'Standardized')


# Prepare the result matrix
clustered = RFM_standardized.copy()

# Decide the number of cluisters
inertia = []
for i in range(1,10):
  kmeans_cluster = KMeans(n_clusters = i).fit(RFM_standardized)
  inertia.append(kmeans_cluster.inertia_)

# Plot the result
plt.plot(range(1,10), inertia)
plt.xlabel('# of clusters(k)')
plt.ylabel('Inertia')
plt.title('The best k lies between 4 to 7', fontsize=20);


''' Modeling '''
# K-means Clustering
kmeans_cluster = KMeans(n_clusters = 7, random_state=100).fit(RFM_standardized)  
clustered['Cluster_Kmeans'] = [str(label) for label in kmeans_cluster.labels_]

# Agglomerative Clustering
agg_cluster = cluster.AgglomerativeClustering(n_clusters = 7, linkage= 'complete').fit(RFM_standardized)
clustered['Cluster_Agg'] = [str(label) for label in agg_cluster.labels_]

# Spectral Clustering
spectral_cluster = cluster.SpectralClustering(n_clusters=7, affinity="nearest_neighbors", random_state=10).fit(RFM_standardized) 
clustered['Cluster_Spectral'] = [str(label) for label in spectral_cluster.labels_]


''' Plotting '''
def plot_cluster(model, name):
  fig = px.scatter_3d(clustered, x='Recency', y='Frequency', z='log(Monetary)', color=model, width=600, height=500, title=name)
  fig.update_layout(paper_bgcolor="LightSteelBlue") #, margin=dict(l=20, r=20, t=20, b=30)
  fig.show()

plot_cluster(model='Cluster_Kmeans', name='K-means Clustering')
print()
plot_cluster(model='Cluster_Agg', name='Agglomerative Clustering')
print()
plot_cluster(model='Cluster_Spectral', name='Spectral Clustering')


model_name = {'Cluster_Kmeans': 'K-Means clustering',
              'Cluster_Agg': 'Agglomerative clustering',
              'Cluster_Spectral': 'Spectral clustering',
              }

fig, ax = plt.subplots(1, 3, figsize=(12,5))
fig.suptitle('Spectral Clustering Provides a More Evenly Distributed Result', fontsize=20)

for i, (model, figName) in enumerate(model_name.items()):
  ax[i].hist(clustered[model].sort_values(), edgecolor='w')
  ax[i].set_title(figName)
  ax[i].set_xlabel('cluster')
  ax[i].set_ylabel('# of members')
  ax[i].label_outer()  # hide the inner overlapping ticks & labels


# Join raw RFM statistics with clustering result, and merge back to the membership_full table
RFM_clustered = RFM.join(clustered['Cluster_Spectral']).reset_index()

membership_clustered = membership_full.merge(
    RFM_clustered[['Card_ID', 'Recency', 'Frequency', 'Monetary', 'Monetary_Annual', 'Cluster_Spectral']],
    how = 'left',
    left_on = 'Card_ID',
    right_on = 'Card_ID'
)

transactionWithCat_Clustered = transactionWithCat.merge(
    RFM_clustered[['Card_ID', 'Cluster_Spectral']],
    how='left',
    left_on='Card_ID',
    right_on='Card_ID'
)

rewards_clustered = rewards_clean.merge(
    RFM_clustered[['Card_ID', 'Cluster_Spectral']],
    how='left',
    left_on='Card_ID',
    right_on='Card_ID'
)


''' Slice dfs into clusters '''
# Slice the membership table into clusters
MemberClusters = []
for i in range(7):
  MemberClusters.append(membership_clustered[membership_clustered['Cluster_Spectral'] == str(i)])

# Slice the transactions table into clusters
TransClusters = []
for i in range(7):
  TransClusters.append(transactionWithCat_Clustered[transactionWithCat_Clustered['Cluster_Spectral'] == str(i)])

# Slice the rewards table into clusters
RewardClusters = []
for i in range(7):
  RewardClusters.append(rewards_clustered[rewards_clustered['Cluster_Spectral'] == str(i)])


# Group statistics
groups = (membership_clustered
          .groupby('Cluster_Spectral', as_index=False)
          .agg(Size=('Card_ID','size'),
               Revenue=('Monetary_Annual','sum'),
               MidR=('Recency','median'),
               MidF=('Frequency','median'),
               MidM=('Monetary','median'),
               MinR=('Recency','min'),
               MaxR=('Recency','max'),
               MinF=('Frequency','min'),
               MaxF=('Frequency','max'),
               MinM=('Monetary','min'),
               MaxM=('Monetary','max'))
          )

groups['% Members'] = 100 * groups['Size'] / groups['Size'].sum()
groups['% Revenue'] = 100 * groups['Revenue'] / groups['Revenue'].sum()
groups['Customer Value'] = groups['Revenue'] / groups['Size']

# Adding a summarizing label describing each cluster
groups['label'] = ['Quarterly', 
                   'Monthly', 
                   'Most valuable', 
                   'Churned (highest)', 
                   'Semi-annually', 
                   'Churned', 
                   'Most loyal']
groups


fig = px.scatter_3d(groups, x='MidR', y='MidF', z='MidM', color='Cluster_Spectral', width=600, height=500, 
                    title='Visualizing the RFM distribution of each cluster')
fig.update_layout(
        # margin=dict(l=20, r=20, t=20, b=30),
        paper_bgcolor="LightSteelBlue")
fig.show()


# Plot the proportion of member count & revenue by cluster
labels = [(f"{i}. " + groups['label'][i]) for i in range(7)]
x = np.arange(len(labels))
width = 0.3

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(6,8))
fig.suptitle('Cluster 2 & 6 Are Most Valuable in Terms of Avg Spending', fontsize=20)
ax1.bar(x - width / 2, groups['% Members'], width, label='% Members')
ax1.bar(x + width / 2, groups['% Revenue'], width, label='% Revenue')
ax1.set_ylabel('Proportion', fontsize=15)
ax1.legend()
ax1.grid();

# Plot the Average Annual customer value by cluster
ax2.bar(x, groups['Customer Value'], .3, color='firebrick', label='Avg spending')
ax2.tick_params(labelrotation=30, labelsize=10)
ax2.set_xlabel('Clusters', fontsize=15)
ax2.set_ylabel('Avg Annual Customer Value', fontsize=15)
ax2.legend()
ax2.grid()
plt.xticks(x, labels);


# Plot the top 5 address (town) of each cluster
Town = []
for i in range(7):
  Town.append(MemberClusters[i]['Town'].value_counts().head())

fig, ax = plt.subplots(1, 7, figsize=(20,5))
for i in range(7):
  colorlist = ['grey' 
               if town in ['Neihu Dist.', "Da’an Dist."] 
               else 'firebrick' 
               for town in Town[i].index]
  ax[i].bar(Town[i].index, Town[i], color = colorlist)
  ax[i].tick_params(labelrotation=75)
  ax[i].set_title(f"cluster{i}")
  ax[i].set(ylabel='# of members')
  ax[i].label_outer()  # hide the inner overlapping ticks & labels
fig.suptitle("Popular Neighborhoods for Each Cluster aren't quite different (sotre locations in gray)", fontsize=20);


# Plot the age distribution by cluster
fig, ax = plt.subplots(1, 7, figsize=(20,5), sharey = True)
for i in range(7):
  ax[i].hist(MemberClusters[i]['Age'], edgecolor='w', bins=np.arange(0,100,10), density=True)
  ax[i].axvline(x=MemberClusters[i]['Age'].median(), color='r', label='median')
  ax[i].set_title(f"cluster{i}")
  ax[i].set_xticks(range(0,100,10))
  ax[i].legend()
  ax[i].set(xlabel='Age', ylabel='% / 10 of members')
  ax[i].label_outer()  # hide the inner overlapping ticks & labels
  
fig.suptitle('Cluster6 Has a Younger Customer Base (30-40)', fontsize=20);


# Cluster 6 vs. population
plt.figure(figsize=(5, 5))
plt.hist(MemberClusters[6]['Age'], edgecolor='w', bins=np.arange(0,100,10), density=True, label='cluster 6', alpha=.7, color='firebrick')
plt.hist(membership_clustered['Age'], edgecolor='w', bins=np.arange(0,100,10), density=True, label='Population', alpha=.5, color='steelblue')
txt = '''Cluster 6 has more members aged btw
 30-40 & 60-70 than population'''
plt.figtext(.5, .9, txt, ha='center', fontsize=15)
plt.xticks(range(0,100,10))
plt.xlabel('Age')
plt.ylabel('% / 10 of members')
plt.legend();


# Plot the gender ratio by cluster
fig, ax = plt.subplots(1, 7, figsize=(10,5), sharey=True)
for i in range(7):
  genderRatio = 100 * MemberClusters[i]['Gender'].value_counts() / MemberClusters[i]['Gender'].value_counts().sum()
  colorlist = ['firebrick' if sex == 'F' else 'steelblue' for sex in genderRatio.index]
  ax[i].bar(genderRatio.index, genderRatio, color=colorlist)
  ax[i].set_title(f"cluster{i}")
  ax[i].set(ylabel='Proportion (%)')
  ax[i].label_outer()  # hide the inner overlapping ticks & labels
fig.suptitle('Cluster6 Has the Highest % of Female', fontsize=20);


# Zoom in - Cluster 6 vs. population
ratio6 = 100 * MemberClusters[6]['Gender'].value_counts() / MemberClusters[6]['Gender'].value_counts().sum()
ratioPop = 100 * membership_clustered['Gender'].value_counts() / membership_clustered['Gender'].value_counts().sum()
col = ['firebrick' if sex == 'F' else 'steelblue' for sex in genderRatio.index]

plt.figure(figsize=(2.5, 5))
plt.bar(ratio6.index, ratio6, color=col, alpha=.5, label='Cluster 6')
plt.bar(ratioPop.index, ratioPop, color=col, label='Population', width=.4)
plt.legend()
plt.ylabel('Proportion (%)', fontsize=12)
txt = '''Cluster 6 Has Higher %
of Female Than Population '''
plt.figtext(.5, .9, txt, ha='center', fontsize=15);


# Plot the top categories (in terms of $ spending) by cluster (wider transparent bar), compared with population (narrower solid bars)
categoryAmount = []
for i in range(7):
  categoryAmount.append(
      100 * TransClusters[i].groupby('Category').sum()['Amount'].sort_values(ascending=False).head(8) / TransClusters[i].groupby('Category').sum()['Amount'].sum()
  )

# Count across all clusters (population)
categoryAmount.append(
    100 * transactionWithCat.groupby('Category').sum()['Amount'].sort_values(ascending=False).head(8) / transactionWithCat.groupby('Category').sum()['Amount'].sum()
)

# Colormap
items = transactionWithCat.groupby('Category').sum()['Amount'].sort_values(ascending=False).head(8).index.values
colorDict = {items[i]: plt.cm.get_cmap('tab10')(i) for i in range(8)}

# Plot the distribution
txt = '''Cluster2 favors Fashion, Women's Wear & Restaurants
Cluster6 favors Sportswear, Leisure & Accessories'''
fig, ax = plt.subplots(2, 4, figsize=(20,10), sharey=True)
fig.suptitle(x=.5, y=1.0, t='Popular Categories for Each Cluster', fontsize=20)
fig.text(.5, .92, txt, ha='center', fontsize=15)
plt.subplots_adjust(wspace=0.3, hspace=0.7)

# Plot each cluster respectively
for i in range(8):
  colorlist = [colorDict[cat] 
               if cat in colorDict.keys() 
               else 'firebrick' 
               for cat in categoryAmount[i].index]
  axi = ax.ravel()[i]
  
  # Plot Population in the background
  axi.bar(categoryAmount[7].index, categoryAmount[7], color=colorDict.values(), label='Population', width=0.3)
  
  # If not population, plot the cluster in lighter wider bars
  if i != 7:
    axi.set_title(f"cluster{i}", fontsize=20)
    axi.bar(categoryAmount[i].index, categoryAmount[i], color=colorlist, alpha=.3, label=f"cluster{i}")
  else:
    axi.set_title("Population", fontsize=20)
  
  # Labels
  axi.tick_params(labelrotation=75, labelsize=15)
  axi.set_ylabel('% of spending', fontsize=15)
  axi.legend()
  axi.grid();


# Zoom in - Plot individual cluaster against population

def plot_cat_individual(c, txt): # c: cluster index
  plt.figure(figsize=(5, 3))
  # Plot Population in the background
  plt.bar(categoryAmount[7].index, categoryAmount[7], label='Population', width=0.3)
  # plot the specific cluster
  plt.bar(categoryAmount[c].index, categoryAmount[c], alpha=.3, label=f"cluster {c}", color='firebrick')

  # Labels
  plt.xticks(rotation=60, 
             horizontalalignment='right', 
             verticalalignment='top',
             rotation_mode='anchor')
  plt.ylabel('% of spending', fontsize=15)
  plt.legend()
  plt.grid()
  plt.figtext(.5, .95, txt, ha='center', fontsize=15);

txt6 = '''Compared with Population, Cluster 6 Spends More
on Sportswear, Leisurewear, Accessories & KIDS '''

txt2 = '''Compared with Population, Cluster 2 Spends More
on Fashion, Women's Wear & Restaurants '''

# Plot cluster 6
plot_cat_individual(6, txt6)

# Plot cluster 2
plot_cat_individual(2, txt2)


# Plot the points collected by reward campaign type, by cluster
fig, ax = plt.subplots(1, 7, figsize=(20,5), sharey=True)
fig.suptitle('Reward Type Is Not What Differentiates Clusters', fontsize=20)
plt.subplots_adjust(wspace=0.3)

for i in range(7):
  # Sum the amount of points earned per campaign type
  camSum = RewardClusters[i][['Campaign_code', 'points']].groupby('Campaign_code').sum().loc[['Normal', 'Double', 'Triple'], :]
  camRatio = 100 * camSum['points'] / camSum['points'].sum()

  ax[i].bar(camRatio.index, camRatio, color = plt.cm.tab10.colors[:3])#, color=colorlist)
  ax[i].set_ylabel('% of all reward points', fontsize=15)
  ax[i].tick_params(labelrotation=45, labelsize=15)
  ax[i].set_title(f"cluster{i}", fontsize=15)
  ax[i].grid();


# Plot the points collected by reward campaign type, by cluster
fig, ax = plt.subplots(1, 7, figsize=(20,5), sharey=True)
fig.suptitle('None of the clusters spend more during reward booster campaigns', fontsize=20)
plt.subplots_adjust(wspace=0.3)

for i in range(7):
  # Average amount of points earned per campaign type
  camMean = RewardClusters[i][['Campaign_code', 'points']].groupby('Campaign_code').mean().loc[['Normal', 'Double', 'Triple'], :]
  # What we care is whether they SPEND MORE with higher reward campaigns, so divide them back to get the spending proportion
  camMean.loc['Double', 'points'] /= 2
  camMean.loc['Triple', 'points'] /= 3

  ax[i].bar(camMean.index, camMean.points, color = plt.cm.tab10.colors[:3])#, color=colorlist)
  ax[i].set_ylabel('Average spending per visit', fontsize=15)
  ax[i].tick_params(labelrotation=45, labelsize=15)
  ax[i].set_title(f"cluster{i}", fontsize=15)
  ax[i].grid();


# Prepare the data we need - Regeistered_month
membership_clustered['Regeistered_month'] = membership_clustered['Regeistered_month'].dropna().astype(int)

# Calculate the new member by month by cluster
newMemberByMonth = membership_clustered.groupby(['Cluster_Spectral', 'Regeistered_month']).size().sort_index().reset_index().rename(columns={0: '# of new members'})

# Plot - cluster 2 & 6
fig = px.line(newMemberByMonth, 
              x='Regeistered_month', 
              y='# of new members', 
              color='Cluster_Spectral', 
              title='Most cluster 2 & 6 are acquired in November & September respectively',
              width=600, height=400)

# Plot - other clusters
fig1 = px.area(newMemberByMonth, 
              x='Regeistered_month', 
              y='# of new members', 
              color='Cluster_Spectral', 
              title='Most infrequent members are acquired in Octobor',
              width=600, height=400)

fig.show()
fig1.show()


# Plot the percentage sales by category
salesByCat = transactionWithCat[['Category', 'Amount']].groupby('Category').sum().sort_values('Amount', ascending=False)
salesByCat['%'] = 100* salesByCat['Amount'] / salesByCat['Amount'].sum()

plt.bar(salesByCat.index, salesByCat['%'])
plt.xlabel('Category', fontsize=15)
plt.ylabel('% of sales', fontsize=15)
plt.title('Fashion is the biggest source of sales', fontsize=25)
plt.grid()
plt.xticks(rotation=90, fontsize=15);

	Member_ID	Gender	Income	Marriage	Education	Vocation	Age	Country	Zipcode	Regeistered_date	Receive_DM	Newsletter	New_product_notification	是否同意關企企業應運用	是否收到簡訊	是否收到EMAIL	是否收到電訪
0	2020012900026157	F	-	-	-	-	51	Taiwan	234	2020/01/29	一定要收到	-	-	False	False	False	False
1	2016101600004320	M	-	-	-	-	49	Taiwan	206	2016/10/16	一定要收到	-	-	True	True	True	True

	Member_ID	Card_ID
0	2020012900026157	9550020011938118
1	2016101600004320	9660020012084142

	Card_ID	SiteID_CounterID	TRANSTYPE	集點次數	折抵次數	來客數_集點	集點狀態	集點	折抵	銷售金額_集點	折抵金額_集點	Campaign
0	9550020012833052	955010018	2	1	0	1	前台交易	1	0	0	0	會員紅利積點(all)
1	9550020012833052	955010018	2	1	0	1	前台交易	4	0	0	0	會員紅利積點(all)

	Card_ID	Date	Amount	Business_ID_Date
0	9660020012071433	2019/12/31	390	00011018_2019/12/31
1	9660020012071433	2019/12/31	500	00011018_2019/12/31

	Business_ID	Counter_ID	Category	SITE_ID	Branch
0	00000035	60001	Restaurants	9770	禮客台中店
1	00000035	60001	Restaurants	9880	禮客內湖2店

DATA ANALYTICS PROJECT - OUTLET MEMBERSHIP PROGRAM¶

DATASET: Local outlet chain data including¶

QUESTIONS¶

ANALYTICAL FRAMEWORK¶

Notebook Setup¶

Read Datasets¶

I. Data Cleaning¶

Membership Table¶

Transaction Table¶

Rewards Table¶

II. Feature Engineering¶

Tagging members with RFM scores¶

III. Modeling - RFM clustering¶

Decide the number of clusters (k) with elbow method¶

Clustering with 3 machine learning models (K-Means, Agglomerative Clustering & Spectral Clustering)¶

Compare different clustering models¶

Tagging Data with Clusters¶

IV. Understading the clusters¶

V. Behavior Patterns for Each Cluster¶

Geography¶

Age¶

Gender¶

Category¶

Reward Campaign¶

Registration Month¶

Sales by category¶

VI. Summary¶

Growth Strategies¶

Reward Program Review¶

Beyond the Dataset¶

	Cluster_Spectral	Size	Revenue	MidR	MidF	MidM	MinR	MaxR	MinF	MaxF	MinM	MaxM	% Members	% Revenue	Customer Value	label
0	0	1867	64287812.0	32.0	5.0	5716.750000	1	85	1	21	3694.400000	8909.300000	28.417047	20.776183	34433.750402	Quarterly
1	1	1345	49869538.0	24.0	10.0	2895.361111	1	154	2	51	369.657143	10988.204082	20.471842	16.116564	37077.723420	Monthly
2	2	1236	96093742.0	39.0	3.0	12614.912500	1	161	1	48	6636.125000	158485.666667	18.812785	31.055049	77745.745955	Most valuable
3	3	395	16688378.0	271.0	1.0	20941.000000	135	365	1	22	10156.000000	220000.000000	6.012177	5.393259	42249.058228	Churned (highest)
4	4	1188	34908968.0	128.0	4.0	7385.166667	70	219	1	22	2270.500000	17588.555556	18.082192	11.281689	29384.653199	Semi-annually
5	5	460	25769527.0	239.0	4.0	6251.000000	150	357	1	94	825.403226	13524.500000	7.001522	8.328055	56020.710870	Churned
6	6	79	21812366.0	3.0	76.0	2467.862595	1	123	45	269	261.552632	9734.692308	1.202435	7.049201	276105.898734	Most loyal